Predictive analytics is a form of data analytics that makes predictions about future events and behaviors. It incorporates historical data, feedback and techniques such as statistical modeling, AI and machine learning. Because the technologies continue to learn and adapt based on the data fed into them, the insights they provide increase over time.

Consider how your phone predicts your next word in a text. You can use the same technology to improve your company's operations and consumer experiences. It takes a little work, of course.

Predictive Analytics Is a Matrix of If/Then Statements

Predictive analytics plays various roles in different fields of work, according to McKay Bird, chief marketing officer at TCN. Bird said organizations use predictive analytics as a tool to generate smooth customer experiences.

"Predictive analytics, in its simplest form, is a decision matrix of if/then statements,” Bird said. “Using IVR (Interactive Voice Response) and integrating CRM, or other customer software, it can better understand how consumers are interacting and then build IVR decisions based on potential actions a consumer can take."

Putting that into practice, Bird said a typical setup would include integrations between call center operations and IT. Depending on the industry and application, the most popular predictive analytics systems would likely integrate with workplace apps like Salesforce, ServiceNow and Zendesk.

Related Article: How Predictive and Prescriptive Analytics Improve the Call Center Experience

Examples of Predictive Analytics

Here are some examples of predictive analytics in customer experience:

Can Human Sentiment Inform Predictive Analytics?

Gal Olevson, vice president of products at Nemesysco, said that predictive analytics can be enhanced by diverse sources, like the data generated by his company’s layered voice analysis technology.

By measuring the customer’s emotional reactions, he added, brands can learn a lot about how they truly feel about the brand, the product, the quality of service and even their preferences in regards to an agent’s approach during conversations – more personal vs. more to the point.

"This type of data can be used," Olevson added, "during live conversations to enable agents to steer clear of topics or behaviors that hurt the customer’s experience, providing instead a tailored customer experience.”

Historical Trends Leads to Marketing Actions

Jason VandeBoom, founder and CEO of ActiveCampaign, sees predictive analytics as “a way for marketers to make predictions about future outcomes based on historical trends and data then take actions based on those insights.”

Using predictive analytics tools are as simple as sending a text message. "If you’re sending a message to your customers in email, SMS or chat, and want to know the best time to send the email, you can use predictive analytics to predict when they’re most likely to engage with it," VandeBoom said. "You can send the content that each customer wants to see, making it a 1:1 experience using predictive analytics by analyzing the type of content they engaged with previously. When customers receive tailored content, they’re more likely to engage and convert." 

Implementing Predictive Analytics into Software Suites

Olevson said that there are many use cases and focal points for predictive analytics. When it comes to implementation, organizations must first define their goals and the data sources available to them. He sees typical integrations of emotion detection technology into existing platforms or new ones using APIs or SDKs.

"The approach to using emotion detection in predictive analytics is to learn how to give customers the best experience possible based on real data," he said. "Some of our users prefer to analyze recordings using our desktop apps while others opt for our cloud services. Implementation, therefore, varies according to the requirements."

Learning Opportunities

In all cases, he added, the data generated is often processed using standard BI systems to extract insights at different resolutions and turn into actionable predictive analytics.

Most CX practitioners can implement and practice predictive analytics, according to VandeBoom. He advises small teams and entrepreneurs to spend 20 minutes a week playing around with a workflow or pipeline. Measure potential actions a customer could take at a given step in their journey.

One of the finest aspects of predictive analytics, he believes, is that it builds on itself. As you forecast more, you may be able to add more complex forecasts and automations, resulting in an even better customer experience over time.

Related ArticleNatural Language Processing and Conversational AI in the Call Center

Why Is Predictive Analytics Important to Customer Experience Leaders?

Predictive analytics uses previously gathered data and trends to predict customers’ needs, desires and potential problems, according to Bird. Stakeholders and leaders must focus on creating efficient processes to solve customer issues as quickly as possible before they even happen. This is ultimately where predictive analytics can mostly benefit customer experience operations, according to Bird. It's all about detecting customer problems before they occur. It can also help predict when a prospective customer will make a purchase.

Predictive analytics is one resource for designing one-to-one experiences for your customers, according to VandeBoom. Leaders can forecast future consumer behavior by evaluating prior behavior and serving them the appropriate content at the right time on the right channel.

Olevson added predicting customer reactions enables CX executives to tailor their customers' journeys and overall experiences to boost customer satisfaction. Most businesses strive for high, consistent and predictable levels of customer satisfaction. His company's voice analytics technology and emotion recognition aims to deliver data that is not skewed by cultural prejudices.

What Role Does Machine Learning Play in Predictive Analytics?

Predictive analytics usually integrates a machine learning algorithm. These machine learning models may be taught to adapt to new data or values over time, resulting in the intended outcomes. Machine learning and predictive analytics are interrelated concepts, according to VandeBoom.

In a customer experience use case, machine learning and predictive analytics work together. Machine learning makes use of previous data to deduce behaviors and trends. "Machine learning gets smarter and more insightful the more you use it," VandeBoom added. Training AI around specific use-cases increases the predictive capabilities of the organization significantly, according to Olevson.

The more data consumed, the more accurate the output is likely to be. "Machine learning has limits and rules," Bird said. "Once those limits and rules are designed, algorithms can be used to learn and further analyze incoming data sets. Predictive analytics and machine learning is complex and depends on the incoming data and its organization.” 

Predictive Analytics Software Tools

In conclusion, we've discovered some predictive analytics software tools that are popular for 2022, according to TechTarget, who listed some popular predictive analytics tools for 2022. They include:

  • H20 Driverless AI: A data science platform that includes automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, bring your own recipe, time-series and automatic pipeline generation for model scoring.
  • IBM Watson Studio: Its open multicloud architecture brings together open source frameworks like PyTorch, TensorFlow and scikit-learn with IBM's ecosystem tools for code-based and visual data science. It includes access to Jupyter notebooks, JupyterLab and CLIs — or in languages such as Python, R and Scala.
  • Microsoft Azure Machine Learning: Data scientists and developers can build, deploy, and manage high-quality models. It includes MLOps (machine learning operations), open-source interoperability and integrated tools.
  • RapidMinder Studio: This tool includes an Auto Model to generate models, access to a code-free workflow designer with 1,500+ algorithms and support for R & Python. It relies on model validation and performance calculations. It integrates machine learning models into existing business applications.